{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "67ebe6b387fca9df",
   "metadata": {},
   "source": [
    "## Clip\n",
    "\n",
    "https://arxiv.org/pdf/2103.00020\n",
    "\n",
    "https://openai.com/index/clip/\n",
    "\n",
    "https://www.modelscope.cn/models/iic/multi-modal_clip-vit-base-patch16_zh\n",
    "\n",
    "https://github.com/OFA-Sys/Chinese-CLIP"
   ]
  },
  {
   "attachments": {
    "efbba6a3-366a-41b6-b696-151201c18c50.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "ac37c312-bf3b-4a93-b881-9a743b61fc43",
   "metadata": {},
   "source": [
    "![image.png](attachment:efbba6a3-366a-41b6-b696-151201c18c50.png)"
   ]
  },
  {
   "cell_type": "code",
   "id": "b05989f300498fcf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T14:11:17.638841Z",
     "start_time": "2025-08-21T14:11:16.669591Z"
    }
   },
   "source": "!pip install cn_clip",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: cn_clip in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (1.5.1)\r\n",
      "Requirement already satisfied: numpy in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from cn_clip) (2.2.6)\r\n",
      "Requirement already satisfied: tqdm in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from cn_clip) (4.67.1)\r\n",
      "Requirement already satisfied: six in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from cn_clip) (1.17.0)\r\n",
      "Requirement already satisfied: timm in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from cn_clip) (1.0.19)\r\n",
      "Requirement already satisfied: lmdb==1.3.0 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from cn_clip) (1.3.0)\r\n",
      "Requirement already satisfied: torch>=1.7.1 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from cn_clip) (2.7.1)\r\n",
      "Requirement already satisfied: torchvision in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from cn_clip) (0.22.1)\r\n",
      "Requirement already satisfied: filelock in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from torch>=1.7.1->cn_clip) (3.18.0)\r\n",
      "Requirement already satisfied: typing-extensions>=4.10.0 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from torch>=1.7.1->cn_clip) (4.14.0)\r\n",
      "Requirement already satisfied: sympy>=1.13.3 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from torch>=1.7.1->cn_clip) (1.14.0)\r\n",
      "Requirement already satisfied: networkx in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from torch>=1.7.1->cn_clip) (3.4.2)\r\n",
      "Requirement already satisfied: jinja2 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from torch>=1.7.1->cn_clip) (3.1.6)\r\n",
      "Requirement already satisfied: fsspec in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from torch>=1.7.1->cn_clip) (2024.9.0)\r\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from sympy>=1.13.3->torch>=1.7.1->cn_clip) (1.3.0)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from jinja2->torch>=1.7.1->cn_clip) (3.0.2)\r\n",
      "Requirement already satisfied: pyyaml in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from timm->cn_clip) (6.0.2)\r\n",
      "Requirement already satisfied: huggingface_hub in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from timm->cn_clip) (0.34.4)\r\n",
      "Requirement already satisfied: safetensors in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from timm->cn_clip) (0.5.3)\r\n",
      "Requirement already satisfied: packaging>=20.9 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from huggingface_hub->timm->cn_clip) (25.0)\r\n",
      "Requirement already satisfied: requests in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from huggingface_hub->timm->cn_clip) (2.32.4)\r\n",
      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from huggingface_hub->timm->cn_clip) (1.1.5)\r\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from requests->huggingface_hub->timm->cn_clip) (3.4.2)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from requests->huggingface_hub->timm->cn_clip) (3.10)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from requests->huggingface_hub->timm->cn_clip) (2.4.0)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from requests->huggingface_hub->timm->cn_clip) (2025.4.26)\r\n",
      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages (from torchvision->cn_clip) (11.2.1)\r\n"
     ]
    }
   ],
   "execution_count": 40
  },
  {
   "cell_type": "code",
   "id": "6d0a9d93f7a127da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-22T04:58:21.290550Z",
     "start_time": "2025-08-22T04:58:17.345828Z"
    }
   },
   "source": [
    "from cn_clip.clip import load_from_name, available_models\n",
    "print(\"Available models:\", available_models())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available models: ['ViT-B-16', 'ViT-L-14', 'ViT-L-14-336', 'ViT-H-14', 'RN50']\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "ViT表示图片编码器用的是Vision Transformer\n",
    "\n",
    "B表示Base Size，中等规模的模型\n",
    "\n",
    "L表示Large Size，大型模型\n",
    "\n",
    "H表示Huge Size，超大型模型\n",
    "\n",
    "16、14表示Patch Size，也就是图片被切之后，小图片的大小是16*16或者14*14\n",
    "\n",
    "336表示该模型在是在336*336的图片上训练的，其他都是在224*224的图片上训练的\n",
    "\n",
    "RN50表示图片编码器用的是ResNet50\n",
    "\n",
    "文本编码器用的是Bert，通过print(model)可以看出来"
   ],
   "id": "e9cfc24c8e98ef81"
  },
  {
   "cell_type": "code",
   "id": "fdd5970e48c49342",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-22T04:58:30.789948Z",
     "start_time": "2025-08-22T04:58:28.744643Z"
    }
   },
   "source": [
    "model, preprocess = load_from_name(\"ViT-B-16\", download_root='./')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading vision model config from /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages/cn_clip/clip/model_configs/ViT-B-16.json\n",
      "Loading text model config from /Users/dadudu/miniconda3/envs/mini-gpt/lib/python3.10/site-packages/cn_clip/clip/model_configs/RoBERTa-wwm-ext-base-chinese.json\n",
      "Model info {'embed_dim': 512, 'image_resolution': 224, 'vision_layers': 12, 'vision_width': 768, 'vision_patch_size': 16, 'vocab_size': 21128, 'text_attention_probs_dropout_prob': 0.1, 'text_hidden_act': 'gelu', 'text_hidden_dropout_prob': 0.1, 'text_hidden_size': 768, 'text_initializer_range': 0.02, 'text_intermediate_size': 3072, 'text_max_position_embeddings': 512, 'text_num_attention_heads': 12, 'text_num_hidden_layers': 12, 'text_type_vocab_size': 2}\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-22T04:58:43.312451Z",
     "start_time": "2025-08-22T04:58:43.308886Z"
    }
   },
   "cell_type": "code",
   "source": "print(model)",
   "id": "819d1b5730794a04",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CLIP(\n",
      "  (visual): VisualTransformer(\n",
      "    (conv1): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16), bias=False)\n",
      "    (ln_pre): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "    (transformer): Transformer(\n",
      "      (resblocks): Sequential(\n",
      "        (0): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (1): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (2): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (3): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (4): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (5): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (6): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (7): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (8): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (9): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (10): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "        (11): ResidualAttentionBlock(\n",
      "          (attn): MultiheadAttention(\n",
      "            (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "          (mlp): Sequential(\n",
      "            (c_fc): Linear(in_features=768, out_features=3072, bias=True)\n",
      "            (gelu): QuickGELU()\n",
      "            (c_proj): Linear(in_features=3072, out_features=768, bias=True)\n",
      "          )\n",
      "          (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "    (ln_post): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
      "  )\n",
      "  (bert): BertModel(\n",
      "    (embeddings): BertEmbeddings(\n",
      "      (word_embeddings): Embedding(21128, 768, padding_idx=0)\n",
      "      (position_embeddings): Embedding(512, 768)\n",
      "      (token_type_embeddings): Embedding(2, 768)\n",
      "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "      (dropout): Dropout(p=0.1, inplace=False)\n",
      "    )\n",
      "    (encoder): BertEncoder(\n",
      "      (layer): ModuleList(\n",
      "        (0-11): 12 x BertLayer(\n",
      "          (attention): BertAttention(\n",
      "            (self): BertSelfAttention(\n",
      "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "            (output): BertSelfOutput(\n",
      "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
      "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "              (dropout): Dropout(p=0.1, inplace=False)\n",
      "            )\n",
      "          )\n",
      "          (intermediate): BertIntermediate(\n",
      "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
      "          )\n",
      "          (output): BertOutput(\n",
      "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
      "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
      "            (dropout): Dropout(p=0.1, inplace=False)\n",
      "          )\n",
      "        )\n",
      "      )\n",
      "    )\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "id": "a26f3fd998c1b191",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T14:14:16.058042Z",
     "start_time": "2025-08-21T14:14:16.051893Z"
    }
   },
   "source": [
    "from PIL import Image\n",
    "\n",
    "image = preprocess(Image.open(\"pokemon.jpeg\")).unsqueeze(0)\n",
    "print(image.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 224, 224])\n"
     ]
    }
   ],
   "execution_count": 45
  },
  {
   "cell_type": "code",
   "id": "5c908a415dcab265",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T14:15:08.354332Z",
     "start_time": "2025-08-21T14:15:08.349776Z"
    }
   },
   "source": [
    "import cn_clip.clip as clip\n",
    "\n",
    "text = clip.tokenize([\"杰尼龟\", \"妙蛙种子\", \"小火龙\", \"皮卡丘\"])\n",
    "print(text.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 52])\n"
     ]
    }
   ],
   "execution_count": 48
  },
  {
   "cell_type": "code",
   "id": "cba9198466efd2b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T14:15:34.870634Z",
     "start_time": "2025-08-21T14:15:34.584495Z"
    }
   },
   "source": [
    "import torch\n",
    "with torch.no_grad():\n",
    "    logits_per_image, logits_per_text = model.get_similarity(image, text)"
   ],
   "outputs": [],
   "execution_count": 49
  },
  {
   "cell_type": "code",
   "id": "49e688a98f0f0f47",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T14:16:10.051453Z",
     "start_time": "2025-08-21T14:16:10.044821Z"
    }
   },
   "source": [
    "# 每张图片与其他文本的相似度，只有一张图片，所以只有一行\n",
    "logits_per_image"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[38.8393, 42.5954, 39.2670, 47.9360]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 50
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7bbd26c69522ac6a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T13:56:21.379965Z",
     "start_time": "2025-08-21T13:56:21.376957Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[39.2461],\n",
       "        [44.1662],\n",
       "        [40.8005],\n",
       "        [46.2530]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个文本与其他图片的相似度，有四个文本，所以有四行，只有一张图片，所以只有一列\n",
    "logits_per_text"
   ]
  },
  {
   "cell_type": "code",
   "id": "6767da0bf055b6c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T14:18:15.893236Z",
     "start_time": "2025-08-21T14:18:15.883970Z"
    }
   },
   "source": [
    "probs = logits_per_image.softmax(dim=-1).cpu().numpy()\n",
    "print(\"Label probs:\", probs)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label probs: [[1.11466186e-04 4.76864353e-03 1.70963802e-04 9.94948983e-01]]\n"
     ]
    }
   ],
   "execution_count": 51
  },
  {
   "cell_type": "code",
   "id": "6cb2e798fdb72e20",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T14:19:50.822178Z",
     "start_time": "2025-08-21T14:19:50.484123Z"
    }
   },
   "source": [
    "image = preprocess(Image.open(\"数字0.png\")).unsqueeze(0)\n",
    "text = clip.tokenize([\"数字5\", \"数字1\", \"数字2\", \"数字0\"])\n",
    "\n",
    "with torch.no_grad():\n",
    "    logits_per_image, logits_per_text = model.get_similarity(image, text)\n",
    "    probs = logits_per_image.softmax(dim=-1).cpu().numpy()\n",
    "\n",
    "print(\"Label probs:\", probs)  # [[1.268734e-03 5.436878e-02 6.795761e-04 9.436829e-01]]"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label probs: [[0.03489063 0.1686822  0.20412728 0.59229994]]\n"
     ]
    }
   ],
   "execution_count": 52
  },
  {
   "cell_type": "code",
   "id": "87c4056de6a19ba6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-21T14:23:46.805652Z",
     "start_time": "2025-08-21T14:23:46.571024Z"
    }
   },
   "source": [
    "# 基于Clip开发一个文搜图的应用\n",
    "images = [Image.open(f) for f in [\"pokemon.jpeg\", \"数字0.png\", \"数字5.png\"]]\n",
    "image1 = preprocess(Image.open(\"pokemon.jpeg\")).unsqueeze(0)\n",
    "image2 = preprocess(Image.open(\"数字0.png\")).unsqueeze(0)\n",
    "image3 = preprocess(Image.open(\"数字5.png\")).unsqueeze(0)\n",
    "# 将三张图片合并为一个tensor\n",
    "image = torch.cat([image1, image2, image3], dim=0)\n",
    "\n",
    "# 定义一个search函数\n",
    "def search(text):\n",
    "    with torch.no_grad():\n",
    "        text = clip.tokenize([text])\n",
    "        _, logits_per_text = model.get_similarity(image, text)\n",
    "        return logits_per_text.softmax(dim=-1).cpu().numpy()\n",
    "\n",
    "search(\"黄色的图片\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.9728942e-01, 1.8365056e-03, 8.7403430e-04]], dtype=float32)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 57
  },
  {
   "cell_type": "markdown",
   "id": "dfc8f9fb6bd54547",
   "metadata": {},
   "source": [
    "## 为什么Clip的图片向量和文本向量会相似？\n",
    "因为Clip模型训练的时候就是想学会这个效果。"
   ]
  }
 ],
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